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Dual‐Objective Item Selection Criteria in Cognitive Diagnostic Computerized Adaptive Testing
Author(s) -
Kang HyeonAh,
Zhang Susu,
Chang HuaHua
Publication year - 2017
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/jedm.12139
Subject(s) - computerized adaptive testing , divergence (linguistics) , kullback–leibler divergence , mutual information , computer science , dual (grammatical number) , item response theory , entropy (arrow of time) , information theory , cognition , artificial intelligence , set (abstract data type) , selection (genetic algorithm) , trait , machine learning , statistics , psychometrics , mathematics , psychology , art , philosophy , linguistics , physics , literature , quantum mechanics , neuroscience , programming language
The development of cognitive diagnostic‐computerized adaptive testing (CD‐CAT) has provided a new perspective for gaining information about examinees' mastery on a set of cognitive attributes. This study proposes a new item selection method within the framework of dual‐objective CD‐CAT that simultaneously addresses examinees' attribute mastery status and overall test performance. The new procedure is based on the Jensen‐Shannon (JS) divergence, a symmetrized version of the Kullback‐Leibler divergence. We show that the JS divergence resolves the noncomparability problem of the dual information index and has close relationships with Shannon entropy, mutual information, and Fisher information. The performance of the JS divergence is evaluated in simulation studies in comparison with the methods available in the literature. Results suggest that the JS divergence achieves parallel or more precise recovery of latent trait variables compared to the existing methods and maintains practical advantages in computation and item pool usage.